Grasp as You Say: Language-guided Dexterous Grasp Generation
Authors: Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing, Xian-Tuo Tan, Xiao-Ming Wu, Hao Li, Mark Cutkosky, Wei-Shi Zheng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on Dex GYSNet and real world environments for validation. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Sun Yat-sen University, China 2 Stanford University, USA 3 Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China |
| Pseudocode | No | The paper describes methods and frameworks in text and diagrams but does not include explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | We promise to release all code and the complete dataset after the publication of this paper. |
| Open Datasets | Yes | We first collect object meshes and human grasps data from existing datasets [27]. |
| Dataset Splits | Yes | We split the Dex DYSNet dataset at the object instance level, using 80% of the objects within each category for training and 20% for evaluation. |
| Hardware Specification | Yes | All experiment are implemented with Py Torch on a single RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | For training our framework, the training epochs are set to 100 for intention and diversity grasp component and 20 for Quality Grasp Component. The loss weights are configured as follows: λ2 para = λ3 para = 10, λ2 chamfer = λ3 chamfer = 1, λ3 cmap = 10, λ3 pen = 100, λ3 spen = 10. Throughout all training processes, the model is optimized with a batch size of 64 using the Adam optimizer, with a weight decay rate of 5.0 10 6. The initial learning rate is 2.0 10 4 and decay to 2.0 10 5 using a cosine learning rate [52] scheduler. |